Embedding invisible hyperlinks or hidden codes in images to replace QR codes has become a hot topic recently. This technology requires first localizing the embedded region in the captured photos before decoding. Existing methods that train models to find the invisible embedded region struggle to obtain accurate localization results, leading to degraded decoding accuracy. This limitation is primarily because the CNN network is sensitive to low-frequency signals, while the embedded signal is typically in the high-frequency form. Based on this, this paper proposes a Dual-Branch Dual-Head (DBDH) neural network tailored for the precise localization of invisible embedded regions. Specifically, DBDH uses a low-level texture branch containing 62 high-pass filters to capture the high-frequency signals induced by embedding. A high-level context branch is used to extract discriminative features between the embedded and normal regions. DBDH employs a detection head to directly detect the four vertices of the embedding region. In addition, we introduce an extra segmentation head to segment the mask of the embedding region during training. The segmentation head provides pixel-level supervision for model learning, facilitating better learning of the embedded signals. Based on two state-of-the-art invisible offline-to-online messaging methods, we construct two datasets and augmentation strategies for training and testing localization models. Extensive experiments demonstrate the superior performance of the proposed DBDH over existing methods.

翻译：将隐形超链接或隐藏编码嵌入图像以替代二维码，已成为近期研究热点。该技术需在解码前首先定位拍摄照片中的嵌入区域。现有方法通过训练模型定位隐形嵌入区域，但难以获得精确的定位结果，导致解码精度下降。这一局限性主要源于卷积神经网络对低频信号敏感，而嵌入信号通常表现为高频形式。基于此，本文提出一种专为隐形嵌入区域精确定位设计的双分支双头（DBDH）神经网络。具体而言，DBDH采用包含62个高通滤波器的底层纹理分支捕捉嵌入引起的高频信号，并利用高层语义分支提取嵌入区域与正常区域的判别性特征。DBDH通过检测头直接检测嵌入区域的四个顶点，同时引入额外分割头在训练过程中对嵌入区域掩膜进行分割。分割头为模型学习提供像素级监督，促进对嵌入信号的更有效学习。基于两种最先进的离线到在线隐形信息传递方法，我们构建了两个数据集及相应的扩增策略用于定位模型的训练与测试。大量实验证明，所提DBDH方法相较于现有方法具有显著优越性。